人工智能与脑血管病

人工智能(artificial intelligence,AI)正助力全球医疗保健的进步。全球脑血管疾病负担重,优质脑血管病医疗资源分布不均,尤其在发展中国家。利用机器学习算法等开发基于AI的诊疗工具,并将经过验证的AI工具应用于辅助临床决策是改进脑血管病医疗服务质量的一项重要举措,也是未来AI在脑血管病领域研究的一个重要方向。目前,AI已在脑血病影像、电子病历等医疗大数据分析等方面取得了一定的研究进展。AI技术在脑血管病疾病风险预测、辅助诊断、治疗决策及预后预测等多个方面均表现出巨大的应用潜力。在AI工具的开发过程中,建立高质量、标准化的脑血管病大数据平台和多中心临床研究及验证网络是主要的难题。将AI工具应用于医疗保健也将伴随一系列挑战,例如:数据安全、隐私、道德、责任、行政法规以及对AI算法“黑箱”的不可解释性等问题,均有待未来的研究进一步去规范和完善。     

机器学习模型在急性缺血性卒中并发症及预后预测中的研究进展

随着医疗数据的不断集成和计算机运算能力的大幅提升,基于机器学习的卒中预测研究逐渐成为交叉学科中的研究热点。相较于传统量表评分,机器学习模型具有快速、准确、可重复性等优势,已被用于卒中的诊断和预后预测,帮助临床医师准确判断患者病情及预后。本文介绍了目前机器学习算法用于急性缺血性卒中并发症及预后预测的研究进展,并分析了当前研究存在的问题,如研究数量不足、样本量过少、缺少外部验证等。     

人工智能临床决策支持系统在脑血管病中的应用

随着科学技术的发展,人工智能（artificial intelligence,AI）应用于脑血管医疗领域将有助于 减轻中国不断加剧的脑血管病疾病负担。临床决策支持系统（clinical decision support system,CDSS）是AI 在医疗领域的一项重要实践应用。利用AI和医疗大数据开发临床决策支持工具,并通过将临床信息与 知识库相匹配,提供基于循证证据的优化诊疗方案。在脑血管病的临床诊疗过程中,CDSS可以辅助高 危人群识别、急性期再灌注治疗决策支持、实现自动化病因分型以及二级预防策略的制定等,在提高 脑血管病的医疗质量、改善患者结局方面发挥重要作用,可能成为未来脑血管病疾病管理的一项重要 辅助工具。     

机器学习在网络社交平台自杀预测领域的研究进展

本文针对机器学习在网络社交平台自杀预测领域的相关成果进行系统综述,为群体及个体自杀预测提供参考。本文将从机器学习在多个平台自杀预测的现状（博客与轻博客、熟人社交平台、论坛、图片与视频社交平台、临床数据库）和局限性（算法准确性和效率、隐私泄露、污名化问题）等方面进行阐述。     

近红外光谱成像在脑血管病诊疗中的应用价值

对脑血管病患者的脑组织氧合情况实施床旁监测,对脑血管病的诊治及预后改善有重要价值。近红外光谱成像（near infrared spectroscopy,NIRS）作为一项新兴的脑成像技术,具有价格低廉、操作便捷、非侵入性等优点,逐渐成为脑血管病研究的焦点。本文介绍了NIRS的基本原理,探讨了NIRS在急性脑梗死早期识别、梗死后再灌注疗效评估、大面积脑梗死继发恶性脑水肿预测等方面的应用价值,预测了NIRS在大脑近皮层出血病情监测中的应用前景。此外,在蛛网膜下腔出血诊疗领域,NIRS通过动态监测脑氧饱和度,在迟发性脑缺血监测及术后功能结局预测等方面也有着较大的应用潜力。最后,本文基于NIRS的优势和不足,展望了未来适合NIRS应用的临床场景。加强NIRS在脑血管病领域的研究,将使实时评估脑血管病院前、院内的精准动态成为可能。     

脑地形图在早期缺血性脑血管病中的应用价值

目的探讨脑地形图(BEAM)在早期缺血性脑血管病的临床应用价值。方法对58例缺血性脑血管病患者在发病后24h内先螺旋CT扫描，再进行MILI、DWI及BEAM检查。结果58例患者在发病24h内，未见异常51例，显示低密度灶7例。再经过MRI和DWI检查，发现皮质梗死20例，皮质下梗死38例。以此作为分组依据，分为皮质梗死组和皮质下梗死组，BEAM在皮质梗死组20例中，19例有病变侧的局限性慢波功率明显增高；皮质下梗死组38例中，仅4例有局限性慢波功率增高，其他34例均属于正常范围脑地形图。经校正卡方检验，二组有显著统计学差异。结论BEAM能够早期发现皮质脑梗死，有助于脑梗死患者早期诊断，以便制定更好的治疗方案和判断预后。     

Apelin-13在脑血管病中的研究进展

脑血管病发病率逐年增高,病死率、致残率居高不下,严重影响人类的健康。研究表明Apelin-13参与脑血管病发病过程,由Apelin-13和Apelin受体APJ组成的Apelin-13/APJ信号通路对脑血管病具有保护作用,有望为脑血管病提供潜在的治疗靶点。本文主要通过其对缺血性和出血性脑血管病的影响进行综述。     

小剂量甘露醇在急性脑血管病中的应用

近3年来我们对小剂量与常规剂量甘露醇治疗急性脑血管病的效果进行了对比研究,现将结果报告如下。 资料和方法 一、病例选择:将1987～1989年内住院并经颅脑CT证实的急性脑血管病人依序编号分组,奇数为小剂量甘露醇治疗组,偶数为常规剂量组。小剂量组     

机器学习在脑卒中诊断与治疗中的应用进展

随着人工智能技术的迅速发展,机器学习算法广泛应用于脑卒中诊断、治疗、预后各阶段,并表现出巨大的应用潜力。本文从机器学习算法研究,机器学习算法在脑卒中诊断与分类诊断、结局预测、风险因素识别等方面总结机器学习算法在脑卒中诊断与治疗中的应用现状并展望未来发展方向。     

超声新技术在脑血管病中的应用

脑血管病是一种高发病率、高病死率以及高致残率的疾病。据统计我国现患脑血管病约600万,每年新发脑血管病130万人,死亡近100万人,约75％幸存者留下偏瘫等后遗症[1]。近年来随着超声影像技术迅猛发展,超声影像技术与脑血管病防治的临床关系已经越来越密切。     

Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke

BackgroundMachine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities.Aims582 studies were identified on initial searching of the PubMed database. Of these studies, 106 full texts were assessed after title and abstract screening which resulted in 489 papers excluded. Of these 106 studies, 79 were excluded due to using cohorts from outside the United States or being review articles or editorials. 27 studies were thus included in this analysis.Summary of reviewOf the 27 studies included, 7 (25.9%) used patient data from California, 6 (22.2%) were multicenter, 3 (11.1%) were in Massachusetts, 2 (7.4%) each in Illinois, Missouri, and New York, and 1 (3.7%) each from South Carolina, Washington, West Virginia, and Wisconsin. 1 (3.7%) study used data from Utah and Texas. These were qualitatively compared to a CDC study showing the highest distribution of stroke in Mississippi (4.3%) followed by Oklahoma (3.4%), Washington D.C. (3.4%), Louisiana (3.3%), and Alabama (3.2%) while the prevalence in California was 2.6%.ConclusionsIt is clear that a strong disconnect exists between the datasets and patient cohorts used in training machine learning algorithms in clinical research and the stroke distribution in which clinical tools using these algorithms will be implemented. In order to ensure a lack of bias and increase generalizability and accuracy in future machine learning studies, datasets using a varied patient population that reflects the unequal distribution of stroke risk factors would greatly benefit the usability of these tools and ensure accuracy on a nationwide scale.     

Machine Learning for Brain Stroke: A Review

Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Therefore, the aim of this work is to classify state-of-arts on ML techniques for brain stroke into 4 categories based on their functionalities or similarity, and then review studies of each category systematically. A total of 39 studies were identified from the results of ScienceDirect web scientific database on ML for brain stroke from the year 2007 to 2019. Support Vector Machine (SVM) is obtained as optimal models in 10 studies for stroke problems. Besides, maximum studies are found in stroke diagnosis although number for stroke treatment is least thus, it identifies a research gap for further investigation. Similarly, CT images are a frequently used dataset in stroke. Finally SVM and Random Forests are efficient techniques used under each category. The present study showcases the contribution of various ML approaches applied to brain stroke.     

Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing

Objective: The manual adjudication of disease classification is time-consuming, error-prone, and limits scaling to large datasets. In ischemic stroke (IS), subtype classification is critical for management and outcome prediction. This study sought to use natural language processing of electronic health records (EHR) combined with machine learning methods to automate IS subtyping. Methods: Among IS patients from an observational registry with TOAST subtyping adjudicated by board-certified vascular neurologists, we analyzed unstructured text-based EHR data including neurology progress notes and neuroradiology reports using natural language processing. We performed several feature selection methods to reduce the high dimensionality of the features and 5-fold cross validation to test generalizability of our methods and minimize overfitting. We used several machine learning methods and calculated the kappa values for agreement between each machine learning approach to manual adjudication. We then performed a blinded testing of the best algorithm against a held-out subset of 50 cases. Results: Compared to manual classification, the best machine-based classification achieved a kappa of .25 using radiology reports alone, .57 using progress notes alone, and .57 using combined data. Kappa values varied by subtype being highest for cardioembolic (.64) and lowest for cryptogenic cases (.47). In the held-out test subset, machine-based classification agreed with rater classification in 40 of 50 cases (kappa .72). Conclusions: Automated machine learning approaches using textual data from the EHR shows agreement with manual TOAST classification. The automated pipeline, if externally validated, could enable large-scale stroke epidemiology research.     

Machine Learning Analysis of MicroRNA Expression Data Reveals Novel Diagnostic Biomarker for Ischemic Stroke

ObjectivesIschemic stroke (IS) is one of the leading causes of morbidity and mortality worldwide. Circulating microRNAs have a potential as minimally invasive biomarkers for disease prediction, diagnosis, and prognosis. In this study, we sought to use different machine learning algorithms to identify an optimal model of microRNA by integrating the expression data of pre-selected microRNAs for discriminating patients with IS from controls.MethodsThe expression level of microRNAs in the peripheral blood of 50 patients with IS and 50 matched controls were assessed through real-time polymerase chain reaction (qRT-PCR). Machine learning algorithms, including artificial neural network, random forest, extreme gradient boosting, and support vector machine (SVM) were employed via R 3.6.3 software to establish diagnostic models for IS.ResultsThe IS group had significantly increased expression levels of miR-19a (P < 0.001), miR-148a (P < 0.001), miR-320d (P = 0.003), and miR-342-3p (P < 0.001) compared with the control group. MiR-148a, miR-342-3p, miR-19a, and miR-320d yielded areas under the receiver operating characteristic curve (AUC) of 0.872, 0.844, 0.721, and 0.673, respectively, with 0.740, 0.940, 0.740, and 0.840 sensitivity and 0.920, 0.640, 0.600, and 0.440 specificity, respectively. Model miR-148a + miR-342-3p + miR-19a had the best predictive value when analyzed via SVM algorithm with AUC, sensitivity, and specificity values of 0.958, 0.937, and 0.889, respectively.ConclusionThe diagnostic value of the combination of miR-148a, miR-342-3p, and miR-19a through SVM algorithm has the potential to serve as a feasible approach to promote the diagnosis of IS.     

Prediction of Motor Function in Stroke Patients Using Machine Learning Algorithm: Development of Practical Models

BackgroundMachine learning (ML) techniques are being increasingly adopted in the medical field.ObjectiveWe developed a deep neural network (DNN) model and applied 2 well-known ML algorithms, logistic regression and random forest, in predicting motor outcome at 6 months after stroke.MethodsIn the present study, by using 14 input variables which are easily measured by clinicians, we developed ML models and investigated their applicability to predicting motor outcome in hemiplegic stroke patients. We retrospectively analyzed data of 1,056 consecutive stroke patients. Favorable outcomes of the upper and lower limbs were defined as a modified Brunnstrom classification (MBC) score of ≥5 (able to perform activities of daily living with the affected upper limb) and a functional ambulation category (FAC) score of ≥4 (able to walk without guardian's assistance), respectively. Poor outcomes of the upper and lower limbs were defined as MBC and FAC scores of <5 and <4, respectively. We developed 3 ML algorithms, namely the DNN, logistic regression, and random forest.ResultsRegarding the prediction of upper limb function, for the DNN model, the area under the curve (AUC) was 0.906. For the logistic regression and random forest models, the AUC were 0.874 and 0.882, respectively. For the prediction of lower limb function, for the DNN, logistic regression, and random forest models, the AUCs were 0.822, 0.768, and 0.802, respectively.ConclusionsWe demonstrated that the ML algorithms, particularly the DNN, can be useful for predicting motor outcomes in the upper and lower limbs at 6 months after stroke.     

基于机器学习的新发急性缺血性卒中患者1年功能预后预测研究

目的 建立基于机器学习算法的新发急性缺血性卒中（acute ischemic stroke,AIS）患者1年预后的预测模型,为相关研究和临床工作提供借鉴。      

机器学习基于不平衡数据预测急性新发缺血性卒中患者院内死亡模型研究

目的 探索利用机器学习基于不平衡数据预测急性新发缺血性卒中患者的院内死亡风险,并比较机器学习模型和传统logistic模型的预测性能.方法 以中国卒中联盟多中心登记数据库中急性新发缺血性卒中患者为研究对象,分别基于机器学习[XGBoost模型、CatBoost模型、随机森林模型、支持向量机(support vector...     

远程医疗在卒中救治体系中的应用

近20年来,远程医疗在卒中救治领域中的应用方兴未艾,现已逐步趋向成熟.国外相继启动了一系列远程卒中项目,并制定出具体实践指南,而我国的远程卒中发展相对落后,但在新型冠状病毒肺炎疫情期间发挥了重要作用,未来可期.随着近年来我国逐步建立起卒中救治体系,将远程医疗应用于卒中救治体系成为提高卒中患者救治效率的一种新模式,同时远...